data.datasets.scenario_parameters.parameters.heat()

Complexity

Conditions

5

Size

Total Lines	110
Code Lines	57

Duplication

Lines	0
Ratio	0 %

Importance

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0

"""The module containing all parameters for the scenario table
            "CH4_to_H2": read_costs(costs, "SMR", "efficiency"),
            "H2_feedin": 1,
            "H2_to_CH4": read_costs(costs, "methanation", "efficiency"),
            "OCGT": read_costs(costs, "OCGT", "efficiency"),
            "power_to_Heat": 0.2,     #overall efficiency (20% electrical Input converted into waste-heat); source: project internal assumption Fraunhofer ISE
            "power_to_O2": 0.04,          #O2-transfer efficiency; source:  Sayed Sadat, Modeling Regional Utilization of the electrolysers Co-Products Oxygen and Heat in Germany, 2024
        }
        
        # Insert overnight investment costs
        parameters["overnight_cost"] = {
            "power_to_H2_system": 452_000, #[EUR/MW]  source: project internal assumption Fraunhofer ISE
            "power_to_H2_stack": 0.21 * 452_000, #[EUR/MW] source: project internal assumption Fraunhofer ISE
            "power_to_H2_OPEX": 0.03 * 452_000, #[EUR/MW/a]  3% of CAPEX, source: project internal assumption Fraunhofer ISE
            "H2_to_power": read_costs(costs, "fuel cell", "investment"),
            "CH4_to_H2": read_costs(costs, "SMR", "investment"),
            "H2_to_CH4": read_costs(costs, "methanation", "investment"),
            "H2_feedin": 0,
            "H2_underground": read_costs(
                costs, "hydrogen storage underground", "investment"
            ),
            "H2_overground": read_costs(
                costs, "hydrogen storage tank incl. compressor", "investment"
            ),
            "H2_pipeline": read_costs(costs, "H2 (g) pipeline", "investment"), # [EUR/MW/km] 
            "Heat_exchanger": 25_000, # [EUR/MW_th] cost assumption for one additional heat_exchanger; source: project internal cost assumption by Fraunhofer ISE
            "Heat_pipeline": 400_000, # [EUR/MW/km]; average value for DN100-pipeline; source: L. Zimmermann, MODELLIERUNG DER ABWÄRMENUTZUNG VON ELEKTROLYSEUREN IN DEUTSCHLAND FÜR EINE TECHNO - ÖKONOMISCHE OPTIMIERUNG EINES SEKTOR - GEKOPPELTEN ENERGIESYSTEM, 2024
            "O2_components": 5000, # [EUR] ; source: Sayed Sadat, Modeling Regional Utilization of the electrolysers Co-Products Oxygen and Heat in Germany, 2024 
        }
        
        #overnight_costs for O2_pipeinecosts related to pipeline_diameter
        parameters["O2_pipeline_costs"] = {
            0.5: 500_000,  # EUR/km
            0.4: 450_000,  # EUR/km
            0.3: 400_000,  # EUR/km
            0.2: 350_000,  # EUR/km
            0.0: 300_000,  # EUR/km   (costs for any other pipeline diameter)
        }
        
        # Insert lifetime
        parameters["lifetime"] = {
            "power_to_H2_system": 25,  # source: project internal assumption Fraunhofer ISE
            "power_to_H2_stack": 15,  #85000 hours ~ 15 years; source: project internal assumption Fraunhofer ISE
            "power_to_H2_OPEX": 1, #given as OPEX/year
            "H2_to_power": read_costs(costs, "fuel cell", "lifetime"),
            "CH4_to_H2": read_costs(costs, "SMR", "lifetime"),
            "H2_to_CH4": read_costs(costs, "methanation", "lifetime"),
            "H2_feedin": read_costs(costs, "CH4 (g) pipeline", "lifetime"),
            "H2_underground": read_costs(
                costs, "hydrogen storage underground", "lifetime"
            ),
            "H2_overground": read_costs(
                costs, "hydrogen storage tank incl. compressor", "lifetime"
            ),
            "H2_pipeline": read_costs(costs, "H2 (g) pipeline", "lifetime"),  
            "Heat_exchanger": 20, # assumption based on lifetime heat_exchanger; source: E. van der Roest, R. Bol, T. Fens und A. van Wijk, „Utilisation of waste heat from PEM electrolysers - Unlocking local optimisation, 2023
            "Heat_pipeline": 20,
            "O2_components": 25,  # source: Sayed Sadat, Modeling Regional Utilization of the electrolysers Co-Products Oxygen and Heat in Germany, 2024 
        }

        # Insert annualized capital costs
        parameters["capital_cost"] = {}
        parameters["O2_capital_cost"]= {}

        for comp in parameters["overnight_cost"].keys():
            parameters["capital_cost"][comp] = annualize_capital_costs(
                parameters["overnight_cost"][comp],
                parameters["lifetime"][comp],
                global_settings("eGon2035")["interest_rate"],
            )
        
        for diameter in parameters["O2_pipeline_costs"].keys():
            parameters["O2_capital_cost"][diameter] = annualize_capital_costs(
                parameters["O2_pipeline_costs"][diameter],
                parameters["lifetime"]["O2_components"],
                global_settings("eGon2035")["interest_rate"],
            )
            
        parameters["marginal_cost"] = {
            "CH4": global_settings(scenario)["fuel_costs"]["gas"]
            + global_settings(scenario)["co2_costs"]
            * global_settings(scenario)["co2_emissions"]["gas"],
            "OCGT": read_costs(costs, "OCGT", "VOM"),
            "biogas": global_settings(scenario)["fuel_costs"]["gas"],
            "chp_gas": read_costs(costs, "central gas CHP", "VOM"),
        }

        # Insert max gas production (generator) over the year
        parameters["max_gas_generation_overtheyear"] = {
            "CH4": 36000000,  # [MWh] Netzentwicklungsplan Gas 2020–2030
            "biogas": 10000000,  # [MWh] Netzentwicklungsplan Gas 2020–2030
        }

    elif scenario == "eGon100RE":
        costs = read_csv(2050)
        interest_rate = 0.07  # [p.u.]

        parameters = {
            "main_gas_carrier": "H2",
            "retrofitted_CH4pipeline-to-H2pipeline_share": 0.23,
            # p-e-s result, this value is overwritten if p-e-s is run
        }
        # Insert effciencies in p.u.
        parameters["efficiency"] = {
            "power_to_H2": 0.709,
            "H2_to_power": read_costs(costs, "fuel cell", "efficiency"),
            "CH4_to_H2": read_costs(costs, "SMR", "efficiency"),
            "H2_to_CH4": read_costs(costs, "methanation", "efficiency"),
            "OCGT": read_costs(costs, "OCGT", "efficiency"),
            "power_to_Heat": 0.2,   # source: project internal assumption Fraunhofer ISE
            "power_to_O2": 0.015,   # source:  Sayed Sadat, Modeling Regional Utilization of the electrolysers Co-Products Oxygen and Heat in Germany, 2024
        }